1. Technical Field
The present invention relates to complex systems, and, in particular, to modeling, analyzing, and managing time series dynamics in complex physical systems.
2. Description of the Related Art
Recently, cyber-physical systems (CPS) have risen to prominence with examples including automobile and intelligent transportation systems, medical devices and healthcare systems, smart grid, industrial monitoring, etc. Since CPS integrates computing, communication, and storage capabilities in the monitoring entities, a significant amount of measurement data can now be collected, which contains useful knowledge to provide an opportunity for system self-management. Modern industries, such as power plant systems, chemical systems and a variety of manufacturers, deploy massive sensors to monitor the status of physical systems. As a result, a large amount of time series observations are collected from the massive sensors and an effective method is needed to model the system dynamics from the data. A good modeling of the underlying dynamics is important for a wide range of applications, such as enhancing the understanding of the underlying dynamics, improving predictive capabilities, monitoring the status of the system, and facilitating anomaly detection and failure diagnosis.
There has been research analyzing measurement data from physical systems to improve the self-manageability of systems, and this research may be classified into two categories: domain specific techniques and domain independent solutions. The domain specific techniques mainly rely on system experts to define rules or policies to extract related knowledge from the data, thus a full understanding of the properties of the systems are necessary. Such methods require extensive human involvement and are domain specific. Also, it may be difficult to obtain complete domain knowledge with the increasing of system scale and complexity. The domain independent solutions attempt to extract knowledge from data by general analytic tools rather than systems experts, (e.g., similarity based approach, support vector machine (SVM) based learning method, etc.). However, their solutions need large amount historical data and are computationally expensive.
Since CPS integrates computing, communication, and storage capabilities in the monitoring entities, a significant amount of measurement data can now be collected, which contains useful knowledge to provide an opportunity for system self-management. However, a number of challenges exist to extract knowledge from measurement data. For example, the data usually have thousands or even millions of attributes with each exhibiting different behaviors. Some attributes are also correlated with each other due to the dependencies between system components. It is necessary to have advanced analytic techniques for system measurements to benefit management tasks such as anomaly detection, capacity planning, and so on